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An Intelligent Prediction System for Mobile Source Localization Using Time Delay Measurements

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Document pages: 12 pages

Abstract: In this paper, we introduce an intelligent prediction system for mobilesource localization in industrial Internet of things. The position and velocityof mobile source are jointly predicted by using Time Delay (TD) measurements inthe intelligent system. To predict the position and velocity, the RelaxedSemi-Definite Programming (RSDP) algorithm is firstly designed by dropping therank-one constraint. However, dropping the rank-one constraint leads to producea suboptimal solution. To improve the performance, we further put forward aPenalty Function Semi-Definite Programming (PF-SDP) method to obtain therank-one solution of the optimization problem by introducing the penalty terms.Then an Adaptive Penalty Function Semi-Definite Programming (APF-SDP) algorithmis also proposed to avoid the excessive penalty by adaptively choosing thepenalty coefficient. We conduct experiments in both a simulation environmentand a real system to demonstrate the effectiveness of the proposed method. Theresults have demonstrated that the proposed intelligent APF-SDP algorithmoutperforms the PF-SDP in terms of the position and velocity estimation whetherthe noise level is large or not.

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